Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
Bibliografía ; Corriente de agua ; Hidrología ; Modelo ; Previsión ; Red de drenaje ; Redneural
Bibliography ; Drainage network ; Forecast ; Hydrology ; Model ; Neural network ; Stream ; The 1990's ; The 2000's
This paper traces two decades of neural network rainfall-runoff (NNRF) and streamflow modelling, collectively termed river forecasting, providing an analysis of recent and ongoing computational hydrological modelling developments in NNRF, thereby
shaping a coherent research agenda for NNRF over the next decade. It is clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design
Carbon dioxide ; Climatic change ; Comparative study ; Ecosystem ; Flow ; Forecast ; Model ; Neural network ; Peat bog ; Temperate zone ; Wavelet analysis
The potential of 6 temporal artificial neural networks (ANNs) augmented with and without 3 orthogonal wavelet functions was tested for predicting net ecosystem exchange of carbon dioxide (CO2) based on a long-term eddy covariance (EC) data set
; (4) time-delay neural network, time-lag recurrent network, and recurrent neural network; (5) online learning versus batch learning algorithms; and (6) diel, diurnal, and nocturnal periods. The coefficient of determination, root mean square error
GEO-environmental suitability evaluation of land for urban construction based on a back-propagation neural network and GIS : A case study of Hangzhou
Análisis espacial ; China ; Construcción urbana ; Espacio urbano ; Estrategia de actores ; Gestión del medio ambiente ; Modelo ; Planificación urbana ; Propiedades del suelo ; Redneural ; Riesgo natural ; Sistema de información geográfica
China ; Decision making process ; Environmental management ; Geographical information system ; Model ; Natural hazards ; Neural network ; Soil properties ; Spatial analysis ; Urban area ; Urban construction ; Urban planning ; Zhejiang
Using geo-environmental factors and the land use status of Hangzhou, China, a back-propagation (BP) neural network model for the evaluation of the geo-environmental suitability of land for urban construction was established with a geographic
information system (GIS) and techniques of grid, geospatial, and BP neural network analysis. Four factor groups, comprising 9 separate subfactors of geo-environmental features, were selected for the model : geomorphic type, slope, site soil type, stratum
Spatially distributed modeling of soil organic matter across China : An application of artificial neural network approach
Análisis en componentes principales ; China ; Distribución espacial ; Krigeage ; Materia orgánica ; Modelo ; Propiedades del suelo ; Redneural ; Suelo
China ; Krigeage ; Model ; Neural network ; Organic materials ; Principal components analysis ; Soil ; Soil properties ; Spatial distribution
This study proposed a radial basis function neural networks model (RBFNN), combined with principal component analysis (PCA), to predict the spatial distribution of soil organic matter (SOM) content across China. To assess its feasibility, 6 241 soil
Analyzing high-risk emergency areas with GIS and neural networks : the case of Athens, Greece
Análisis espacial ; Atenas ; Estimación ; Grecia ; Localización ; Redneural ; Riesgo ; Salud ; Sistema de información geográfica
Athens ; Estimation ; Geographical information system ; Greece ; Health ; Location ; Neural network ; Risk ; Spatial analysis
This article combines geographic information systems (GIS) and neural networks for performing health emergency assessments and generating hazard maps that show areas that are potentially at high risk for emergencies. As a result, emergency services
Calibration of cellular automata by using neural networks for the simulation of complex urban systems
Calage ; Dynamique spatiale ; Développement urbain ; Probabilité ; Réseau neural ; Réseau urbain ; Simulation ; Système d'information géographique ; Système urbain
Calibration ; Geographical information system ; Neural network ; Probability ; Simulation ; Spatial dynamics ; Urban development ; Urban network ; Urban system
Les AA. présentent un nouveau modèle d'automates cellulaires à base de réseaux neuraux artificiels pour effectuer le calage et la simulation.
Comparative study ; Eastern United States ; Experiment plot ; Forecast ; Model ; Neural network ; Rill wash ; Soil erosion ; United States of America ; Water erosion ; Watershed
The aim of this study was to investigate the applicability of using neural networks to quantitatively predict soil loss from natural runoff plots. Data from 2879 erosion events from 8 locations in the United States were used. The AA. present
a comparison study between results from erosion and runoff procedures of the WEPP technology (Water Erosion Prediction Project model) and from artificial neural networks. In most cases, the results received from the neural networks were better than those from
WEPP, although the neural networks generally tended to under-predict runoff and soil loss values.
Cet article de synthèse considère l'application des réseaux neuraux artificiels à la modélisation de la pluie et du ruissellement et à la prévision des inondations.
The aim of this study is to propose a novel approach utilizing artificial neural network and fuzzy clustering methods for landslide frequency estimation. This study also investigates the 2005 Saeen, Iran landslide triggered by prolonged heavy
L'élaboration d'un modèle de réseau neural comptable implique deux opérations distinctes, la détermination d'une typologie de réseau et l'estimation des pondérations. Quant à la modélisation des interactions à travers l'espace géographique, les AA
The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment
The assessment methods which can currently be used with GISs have limitations which may lead to inaccurate assessment. An artificial neural network is an effective tool for pattern analysis. It allows decision rules of greater complexity
to be applied in pattern classification. A set of neural networks is described. The integration between the neural networks and a GIS is addressed, and some experimental results are presented and analyzed.
The aims in this study were to estimate bathymetry based on derivative reflectance spectra used as input to a multilayer perceptron artificial neural network (ANN) and to evaluate the efficacy of field and simulated training/testing data. ANNs were
Austria ; Flow ; Gravity model ; Model ; Neural network ; Origin-destination ; Spatial interaction ; Spatial system ; Telecommunications
Modélisation de flux d'interaction spatiale à contrainte unique sous la forme d'un réseau neural. Exemple des flux interrégionaux de télécommunication en Autriche. Développement de la procédure globale de recherche d'estimation des paramètres.